Fruits and vegetables are widely known to be rich in nutrients, antioxidants, vitamins, dietary fiber, minerals, and a bioactive molecule, making them an essential component of a balanced diet with multiple documented positive effects on human health. The probiotication of plant-based juices for the production of functional and nutraceutical food serves as a healthy alternative to dairy probiotics. They are cholesterol free, lack several dairy allergens, and also encourage ingestion for people with lactose intolerance. This review highlights valuable claims regarding the efficacy of different probiotic strains on various diseases. A comprehensive nutrition comparison and the preference of plant-based over dairy probiotic drinks is also discussed, supported with updated market trends of probiotic drinks (dairy and non-dairy based). An extensive compilation of current plant-based probiotic drinks that are available in markets around the world is listed as a reference. The fermentability of carbon sources by probiotic microorganisms is crucial in addressing the development of plant-based drinks. Therefore, the pathway involved in metabolism of sucrose, glucose, fructose, and galactose in fruit and vegetable juice was also underlined. Finally, the key factors in monitoring the quality of probiotic products such as total soluble solids, sugar consumption, titratable acidity, pH, and stability at low storage temperatures were outlined. It is robiotics ,3. Amongobiotics ,5,6,7. Tobiotics . Fermentobiotics .Up until now, the global probiotic drink market has been dominated by dairy-based beverages . HoweverLactobacillus and Bifidobacterium, both widely applied as probiotics, are the main inhabitants of the intestine [L. acidophilus resulted in 109 CFU/mL viable cells after 28 h, and the samples, which were stored at 5 °C, subsequently retained a viable cell count around 10 log CFU/mL on the 16th day, with a significant decrease in pH and sugar content and an increase in acidity and total soluble solids. Meanwhile, Yoon et al. [L. casei, L. plantarum, and L. delbreuckii increased in viable cell count after 72 h of fermentation (around 108 CFU/mL); however, during the refrigeration period, only L. plantarum retained the highest viability (107–108 CFU/mL), while L. delbreuckii reduced in viability to 105 CFU/mL at week 3 and week 4, and L. casei lost its viability after 2 weeks of refrigeration. It is believed that the wide range of fermentable sugars such as glucose, fructose, galactose, sucrose, mannitol, stachyose, and raffinose in fruits and green plants provide multiple options for probiotics to utilize and grow [Lactic acid bacteria (LAB) are bacteria that produce lactic acid as the by-product of the lactic acid fermentation of a substrate ,14. Bactntestine  and are ntestine . Despitentestine . Practicntestine  reportedn et al.  revealedand grow ,21.Lactobacillus and Bifidobacterium, as both constitute most of the normal intestinal microbiota in various mammalian species [Lactobaciili, Enterococci, and Enterobacterium. Anaerobic bacteria such as Bifidobacterium, Bacteroides, and Clostridium will colonize the GIT with an increase in age and compete with the facultative anaerobes, setting the colon environment to favor anaerobe growth over time [‘Probiotics’ is defined by the Food and Agriculture Organization of the United Nations (FAO) and World Health Organization (WHO) as “live microorganisms which when administered in adequate amounts confer a health benefit on the host” . Two of  species . Right aver time .More than 400 bacterial species can be found in a normal adult human GIT. Surprisingly, 50% of the wet weight of human feces is contributed to by bacterial biomass. The intestinal microflora maintain their population density by preventing opportunistic colonization by pathogenic bacteria with a mechanism referred to as “colonization resistance” or “barrier effect” ,24. TherResearch demonstrating the beneficial effects of probiotics to human health has been widely published. Probiotics must grow and/or perform therapeutic activity to benefit the host ,26. Alth9 CFU/g of Bifidobacterium longum BORI and 2 × 109 CFU/g of Lactobacillus acidophilus AD031 compared to the placebo treatment (probiotic-free skim milk). However, the underlying therapeutic mechanism is yet to be investigated. According to an in vitro study conducted by Wang et al. [Bifidobacterium lactis Bb12 at a minimal viable cell count of 9 × 108 CFU/mL suppresses the growth of Helicobacter pylori and decreased the C-urea breath test (UBT) value, suggesting the decreased urease activity of H. pylori. This finding is supported by Midolo et al. [Lactobacillus sp. strongly inhibited the growth of H. pylori NCTC 11637 in vitro in a pH- and concentration-dependent manner. Coconnier et al. [H. pylori with no gastric histopathological lesions in human muco-secreting HT29–MTX cells after the administration of the Lactobacillus acidophilus strain LB spent culture supernatant (LB–SCS).Park et al.  reportedg et al. , it was o et al. , wherebyr et al.  found a 9 and 1011 CFU/mL of probiotics is the effective daily dose. Furthermore, Martins et al. [6–107 CFU/mL should be the minimum number of viable probiotic cells to be ingested daily. Recently, Gangwar et al. [6–108 CFU/mL or g of cells can exert therapeutic effects and meet the daily requirement of probiotics.Ingestion of a minimum dosage of probiotics is recommended to bestow health benefits on the host. As such, manufacturers should indicate a minimum daily dosage and expiry date for each strain of their probiotic products, supported by strong scientific evidence and sales approval . The doss et al.  and Shors et al.  agreed tr et al.  suggeste10 to 1012 CFU of Lactobacillus rhamnosus GG daily was effective in treating acute diarrhea compared to 107 CFU/day. A minimum dose of 108–109 CFU/day showed a reduce rotavirus concentration in patient fecal matter [8 CFU/day of S. thermophilus and 109 CFU/day of B. lactis, however, did not reduce the duration of rotavirus diarrhea [Moreover, a person with a gastrointestinal disease such as acute diarrhea should ingest a larger dose of probiotics daily. Basu et al.  stated tl matter ,33. In cdiarrhea . This suProbiotic administration can regulate the neuropsychological functions of the central nervous system. The bidirectional gut–brain relationship can be influenced by the population of gut microflora. A study by Liu et al.  revealedLactobacillus sp. decreases self-injurious behavior (SIB) in primates. SIB is a complex phenotype that occurs in 10–12% of non-human primates and 7–34% of humans. The condition is caused by abnormally elevated hypothalamic-pituitary-adrenocortical (HPA) axis activity, which causes primates to self-harm such as biting fingers or other body parts during sleeping. The injuries sustained may cause infection in the wild. According to research by McGinn [Lactobacillus reuteri containing an average 200 million CFU/tablet modestly decreased biting behavior. Therefore, the sleeping quality of the monkeys in the SIB group is improved.Administration of y McGinn , the rheTo exert their beneficial effects on the host, probiotic strains need to pass through the harsh conditions of the GIT. Gastric-acid and bile-acid resistance are two main criteria measured in selecting viable probiotic strains. Food travels along the digestive tract starting from the mouth, esophagus, stomach, small intestines, and lastly the large intestine . The stoY. enterocolitica 8081u−b (mutant), dramatically reduced in viability from 114.9 at pH 7.0 to 12.3 at pH 3.5, respectively, with all cells wiped out below pH 3.0. In contrast, the wild-type Y. enterocolitica 8081b also experienced reduced cell viability but was still present even at pH 2.0. The acid resistance of the wild type contributed to its ability to produce urease, while the urease mutant cannot synthesize urease, causing it to be susceptible to acid [Depending on the species and strain, acid sensitivity was observed in most microorganisms at pH levels below 3.0 ,41. Tenn to acid . While sLactobacillus species, one of the main inhabitants of the colonic compartment, are essentially resilient to stomach acid. The human-derived strain L. rhamnosus GG is a commercial probiotic strain that has been shown to survive passage through the highly acidic stomach with a tolerance to acid as low as 2.5 for 4 h [0F1-ATPase as a shielding mechanism against acidic conditions [0F1-ATPase consists of two protein portions of F1 and F0. The F1 portion consists of α, β, γ, δ, and ε subunits responsible for the catalyzation of ATP hydrolysis. The F0 is the integral membrane portion that contains a, b, and c subunits which form membranous channels for proton translocation [0F1-ATPase is expressed at low pH levels and generates a proton motive force via portion expulsion, which reduces cytoplasmic H+ concentration, leading to an increased intracellular pH at low extracellular pH [L. rhamnosus GG in an acidic environment as the probiotic metabolize the sugar through glycolysis to provide sufficient ATP reserves for F0F1-ATPase function in pH homeostasis.Food typically stays in the stomach for around 2 to 4 h before being emptied out during gastric digestion . Lactoba for 4 h . It was nditions . The F0Flocation . The F0Flular pH . Cocoranlular pH  proved tBile acid is produced by hepatocytes through daily cholesterol degradation at approximately 350 mg  to removP. pentosaceus has more than a 90% survival rate compared to L. mesenteroides (less than 40%) after 6 to 24 h of incubation with 0.4% (w/v) bile salts.To counteract the detergent activity of bile salts, enteric bacteria develop bile resistance through the enhancement of the cell membrane by: (1) expression of long O-antigen chains in the lipopolysaccharides ; (2) expProbiotication is a process of inoculating beneficial microorganisms (mostly lactic acid bacteria) into a liquid substrate to manufacture functional beverages, which subsequently adds market value due to the various health benefits of probiotics . ChangesOn the other hand, the fastest market growth was seen in the plant-based product segment over the forecasted period. There is a significant increase in the demand for plant-derived drinks, including probiotic fruit and vegetable juices . The driA dairy-based probiotic drink is a milk supplemented with probiotics . The milArtocarpus champeden, and its hybrid Nanchem and found that the flesh of A. champeden contained higher total carbohydrates (16.2–28.3 g/100 g) compared to Nanchem (7.5–30.0 g/100 g). The crude protein, fat, and ash contents of A. champeden flesh were also relatively higher than Nanchem. In terms of the maturation stage, the unripe A. champeden fruit contained a higher percentage of ash (4.6–5.0%), crude fiber (12.9–23.9%), crude protein (7.3–15.9%), and crude fat (3.9–6.4%) than the ripe flesh. However, the ripe A. champeden fruit stored higher total carbohydrates (16.2–28.3 g/100 g) than the unripe flesh (2.4–5.1 g/100 g).Milk is a nutrient-dense substrate which provides a sufficient supply of carbon (lactose), nitrogen (casein and whey), and mineral sources  ,74 for tEating a diet with high fat and cholesterol greatly increases the risk of developing cardiovascular diseases . The WorCholesterol is important for retaining the fluid mosaic model of mammalian cellular membrane, as it assists the lipid bilayer in terms of permeability, hydrophobicity, and fluidity . HoweverExcessive fatty food consumption along with a sedentary lifestyle and genetics contribute to overweightness and obesity . The CleGenerally, two major milk proteins responsible for sparking an allergic response in humans are casein and whey. The acidification of the cow milk results in two fractions: Fraction 1 appears as solid coagulum (coagulated proteins) called casein contributes to 80% of the total milk protein, and Fraction 2 appears with a liquid consistency (lactoserum) called whey contributes to 20% of the total milk protein ,91,92,93The allergens in milk exhibit immunoglobulin E (IgE) epitope clusters . The IgE12H22O11), a disaccharide made up of glucose and galactose subunits. Lactose makes up 2% to 8% of milk by weight [d-galactosidase which cleaves the glycosidic bonds between the glucose and galactose. However, the expression of lactase decreases with age as humans consume less milk throughout adulthood [The preference of non-dairy- over dairy-based products is more prevalent among lactose-intolerant and vegetarian consumers. Animal abuse, environmental damage, and the search for new taste profiles drive consumers to resort to non-dairy products. Milk contains lactose . The ten probiotics in Plantiful belong to the genera Lactobacillus, Bifidobacterium, and Streptococcus, all of which comprised the normal intestinal microbiota in humans. Therefore, these probiotics can grow in parallel to one another in the same medium, similar to what complex indigenous microbiome performs in the human intestine [Melo-Bolivar et al.  discoverntestine .Bacillus coagulans [B. coagulans spores are resistant to heat, cold temperatures, and stomach acid and remain dormant in the juice but germinate once it passes through the gastric compartment [® applied starter cultures from kombucha (fermented tea), apple cider (fermented apple juice), and sauerkraut brine (fermented cabbage), respectively. The use of fermented substrates already enriched with microbial populations effectively shortens the fermentation period for the next batch of fresh substrate [Biomel, GT’s Organic Kombucha, KeVita Apple Cider Vinegar Tonics, and VitaCup Immunity Coffee Pods contain oagulans ,121,122,partment . While mubstrate .Cereal-based probiotic drinks are basically the juice extracted from cereal products such as millet, oat, and rice that is fermented with a selected probiotic strain. Aside from fruit-and-vegetable-based probiotic beverages, researchers gear toward a cereal-based substrate to serve as a mode of transportation of probiotic strain into the human body. The edible endosperm of a cereal seed contains the ingredient to make a cereal juice. Compared to fruit, extracting cereal juice from the endosperm involves similar steps such as soaking in water for several hours (to increase water content and soften the seed), followed by draining (to remove excess water) and then wet milling (to separate the solid and liquid part). From here, the juice is introduced to heat treatment  before being inoculated and fermented with a pre-selected probiotic .Cereal-based probiotic drinks differ from the ones made of fruit juices. One obvious difference is the production process. It is much easier to extract juice from cereal, as it comes from the endosperm of seeds. On the other hand, fruit juice requires extra processes if it comes from a coconut, which needs to be cracked open and grated. Some fruit source such as mango and papaya need only peeling and seed removal before cutting into smaller pieces. When comparing both cereal and fruit juices, the stability of both fermented drinks depends on the type of substrate and probiotic strain. Fruits generally contain more diverse groups of dietary fibers that serve as prebiotics for the good bacteria in the refrigeration period. However, upon the production process, the nutritional content of the cereal or fruit juice may be altered. The starch granules that present more highly in cereal endosperm may swell up upon heat treatment, causing water to be absorbed in an irreversible manner which leads to starch gelatinization. When the starch gelatinizes, the juice thickens and becomes less favorable to be used in beverages. Heating can also lead to the degradation of bioactive compounds in fruit juice, lowering their medicinal properties.S.thermophilus, L. acidophilus, and Bifidobacterium BB-12. Meanwhile, the Lactobacillus casei shirota, which is usually used in Yakult can also grow and survive in low temperature with a viable cell count around 109 CFU/mL. Fortunately, consumers can benefit more from drinking cereal- or fruit-based probiotic beverages compared to probioticated milk. Cereal- and fruit-based probiotic juices are healthier than an alternative to milk; therefore, they are less in fats and cholesterol with a handful amount of carbohydrates, proteins, dietary fibers, minerals, and vitamins. Moreover, consumers can have a wider selection of flavors, as the source of cereal and fruit-based beverages can vary, some having a unique taste and aroma. In addition, the viability of probiotic drinks in cereal and fruit-based beverages can suffice for the minimum probiotic dosage (above 6 log CFU/mL). Hassan et al.  reportedFermentable sugars are metabolizable sugars used by bacteria during the fermentation period. In plant-based matrices, bacteria can use sucrose, glucose, fructose, and galactose as simple sugars to thrive during the fermentation process, along with soluble fibers as prebiotics to sustain their cellular processes. A diverse range of fermentable carbohydrates in the diet can provide nutritional as well as potential health benefits to humans. The bacterial fermentation of fermentable sugars primarily results in the production of short-chain fatty acids  that are beneficial for GIT health, such as promoting colonic health, as they are involved in the control of colonic mobility, colonic blood flow, and GIT pH, all of which affect nutrient and electrolyte absorption .In plants, the translocation sugars is found in various types of horticulture crops, whereby sucrose is the main sugar translocated between the leaves and the fruits, and sugars such as sorbitol, raffinose, stachyose, and mannitol can be also found . Sugar pThe energy-storing photoassimilate is converted into sucrose and sorbitol before being translocated in the phloem by a transporter. The translocation process applies the pressure-flow theory, where the sugar diffuses from a region of high sugar concentration to a region of low sugar concentration . When thIn the cell cytoplasm, the translocated sugars are subjected to modification. First, the sucrose is broken down into fructose and glucose by invertase and sucrose synthase, sorbitol will be metabolized by sorbitol dehydrogenase, and then sucrose is re-synthesized again by sucrose phosphate synthase . In addi6H12O6. In an oxygen-rich state, the pyruvate generated in glycolysis undergoes oxidation, a citric acid cycle, and an electron transport chain. In oxygen-deprived environments, anaerobic bacteria perform glycolysis to generate adenosine triphosphate (ATP), an energy-storing molecule used for cellular respiration and reproduction. Simply put, the glycolytic pathway involves a series of steps to convert one glucose molecule into two pyruvate molecules. As a result, a net of two ATP molecules is generated from two adenosine diphosphate (ADP) molecules. Simultaneously, two reduced forms of nicotinamide adenine dinucleotide (NADH) are oxidized into two NAD+ molecules [Glucose is the main form of energy for most bacteria . Glucoseolecules . The gly+, however, is a limiting-factor molecule in glycolysis, as it is absent in the cell. Therefore, the lactic acid fermentation serves as an NAD+ regeneration process that recycles the electron acceptor NAD+ from the pyruvate through the lactic acid reduction process back into the glycolysis process for continuous ATP production. In lactic acid fermentation, two pyruvate molecules are reduced into two lactic acid molecules [NADolecules .Fructobacillus spp. Kodama et al. [Fructose is another subcategory of monosaccharides and is expressed as a six-carbon ketone sugar. While most bacteria utilize glucose, some lactic acid bacteria (LAB) have evolved to cope in fructose-rich niches. The bacteria that can metabolize fructose are termed fructophilic lactic acid bacteria (FLAB) which mainly consists of a et al.  was the Fructobacillus spp. does not seem to express the adhE gene needed to produce the enzyme for conversion of acetyl phosphate to ethanol [Fructobacillus tropaeoli F214-1T has the maximum growth curve on glucose under aerobic conditions, followed by glucose in the presence of pyruvate, on fructose media, and lastly the lowest growth curve on glucose. This finding suggests that pyruvate and oxygen are used as the external electron acceptor for FLAB to grow on glucose substrates. Furthermore, FLAB can also use phenolic acids as the external electron acceptors [Normally, an obligately heterofermentative LAB metabolizes glucose through the phosphoketolase pathway, but  ethanol . Moreove ethanol  revealedcceptors . As seend-galactose to α-d-galactose by galactose mutarotase enzyme . Then, α-d-galactose is phosphorylated by galactokinase enzyme  into galactose-1-phosphate. Next, one UMP group from UDP-glucose is transferred to galactose-1-phosphate by galactose-1-phosphate uridyltransferase  to form UDP-galactose and glucose-1-phosphate. The Leloir pathway is completed by the interconversion between UDP-galactose and UDP-glucose by UDP-galactose-4-epimerase , where it can be used again to convert galactose-1-phosphate into glucose-1-phosphate [Galactose is one of the three dietary six-carbon monosaccharides, along with glucose and fructose. LAB is known to metabolize lactose in milk and convert it into glucose and lactose. While glucose is readily used for glycolysis, galactose is less efficiently metabolized. LAB can take up and metabolize galactose in two ways via the Leloir pathway or PEP-transferase system (PTS). The first method involves the importation of galactose into the cell by permease GalP and subsequently channeling into the Leloir pathway reactions . The reahosphate . The gluLacEF (happens in the presence of lactose) or galactose-specific PTS [lacG gene) converts galactose into galactose-6-phosphate. The galactose-6-phosphate is fed into the tagatose-6-phosphate pathway. The tagatose-6-phosphate pathway starts from the isomerization of galactose-6-phosphate by the galactose-6-phosphate isomerase (encoded by lacAB gene) into tagatose-6-phosphate. Then, the tagatose-6-phosphate is phosphorylated into tagatose-1,6-bisphosphate by tagatose-6-phosphate kinase (encoded by lacC gene). The final step in the tagatose-6-phosphate pathway is the cleavage of tagatose-1,6-bisphosphate into a triose dihydroxyacetone-phosphate by tagatose-1,6-bisphosphate adolase (encoded by lacD gene) [The second method of galactose metabolization in LAB is through the lactose-specific PTSific PTS ,142. ThecD gene) ,142. Thed-glucopyranose (glucose) molecule and one unit of β-d-fructofuranose (fructose) molecule with molecular formula, C12H22O11. The unique glycosidic bond is made between one end of reducing sugar and another end of non-reducing sugar inhibits sucrose from a spontaneous reaction with cellular and circulatory molecules. Furthermore, sucrose is a non-reducing sugar due to the absence of anomeric hydroxyl groups. Sucrose occurs naturally in fruits, vegetables, nuts, and commercial crops such as sugar cane and sugar beets [Sucrose is a disaccharide consisting of one unit of α-ar beets .scr) and sucrose permease. The PTS-EIIscr modifies external sucrose into sucrose-6-phosphate and takes it up into the cytosol, while the non-PTS sucrose permease allows entry of the unmodified sucrose [The ability of LAB in sucrose metabolism depends on the expression of specific sucrose permease and/or sucrose-hydrolyzing enzymes. Bacteria can express two types of permeases, which are the phosphotransferase system sucrose-specific EII component (PTS-EII sucrose . AdditioPgm gene). Furthermore, an ATP-dependent fructokinase (encoded by FruK gene) will catalyze the phosphorylation of the sucrose-6-phosphate into fructose-6-phosphate. The glucose-6-phosphate and fructose-6-phosphate are the intermediate in the glycolytic pathway to produce lactic acid [The sucrose-6-phosphate hydrolases causes the cleavage of sucrose-6-phosphate accumulated from the PTS system into glucose-6-phosphate and fructose. Another enzyme secreted is the sucrose phophorylases that belong to the family with 13 members of the glycosyl hydrolases . The unmtic acid . Figure Fermentation is a metabolic process which occurs in yeast and bacteria to convert sugar into alcohol, acids, and/or gases. In the fermentation industry, fermentation is referred to as the bulk growth of microorganisms in a bioreactor supplied with growth medium ,147. In Saccharomyces cerevisiae) [+ by allowing the NADH to donate electrons to pyruvate, causing it to be reduced to lactate and oxidize NADH into NAD+. As a result, the glycolysis process can obtain a steady supply of NAD+ to generate ATP energy [In general, there are two types of fermentation mainly applied in the fermentation industry, namely alcohol fermentation and lactic acid fermentation. Alcoholic fermentation is the oldest of all biotechnological applications. The key player in alcoholic fermentation is yeast (evisiae) . In an oevisiae) ,151. In evisiae) . In contevisiae) . In the P energy .Lactobacillus, Pediococcus, Lactococcus, Streptococcus, Enterococcus, Oenococcus, Leuconostoc, and Weissella. Nonetheless, the genera Lactobacillus and Bifidobacterium are commonly used as probiotics [Lactic acid bacteria (LAB) are mostly Gram-positive, non-motile, and non-spore-producing with rod and cocci shapes. They are facultative bacteria that can grow both in aerobic and anaerobic condition (mostly anaerobic) . LAB exhobiotics , as bothobiotics .Lactobacillus sp. strains CA1 and CA4, are able to grow at a temperature range of −20 °C to 37 °C (psychrophiles), while the other three antimicrobial-producing LAB, coded as CCB1, GB3, and CB3, were able to grow at the temperature range of 28 °C to 70 °C (thermophiles). Gangwar et al. [Bacillus coagulans MTCC 5856 strain, a commercialized spore-forming LAB is thermophilic in nature (growth at 52 °C).Most LAB are mesophiles which can grow at temperature range of 14 °C to 45 °C, where they grow best at optimal temperatures of 35 °C to 39 °C . Some isr et al.  reportedL. acidophilus first shows the lag phase where the viable cell count remained around 4 log CFU/mL from 0 to 4 h of fermentation in coconut milk at 37 °C. Between 4 and 20 h, the growth curve became exponential, where the viable cell count increased from 4 log CFU/mL to a maximum of 9.89 log CFU/mL. The L. acidophilus retained its maximum cell viability until the 28 h fermentation period was over.In the fermentation of non-dairy matrices, probiotic strains are reported to follow the same sigmoid growth curve pattern, measured as a colony-forming unit (CFU) per gram (g) or milliliter (mL) or cell density of the sample as shown in + for the continuous production of ATP in the glycolysis process. The increased number of bacterial cells will replace and add value to the TSS. The refractive index increases due to the slower travel time of light as the cell density increases [The total soluble solid (TSS) is a measurement of the sugar content inside the sample. The TSS value of a sample solution is measured using a refractometer and determined by the index of refraction. The unit of TSS is degrees Brix (°Brix) . Meanwhincreases .Bacillus coagulans and an increase in total soluble solids from 5.0 to 6.0 °Brix. The increase in TSS value is caused by the increased viable cell counts of the probiotics, leading to an increase in the refractive index of the sample from 1.340 to 1.342. A reduction in sugar causes the end-product of a fermented probiotic juice to be less sweet than the unfermented fruit juice [The continuous depletion of fermentable sugars will slow down the exponential growth of the bacteria. At plateau, almost all sugars are depleted, causing a rapid decrease in viability, and the cell enters the death phase due to accumulation of waste. Gangwar et al.  noticed it juice ,69.Titratable acidity is the measurement of total acids inside a food sample . Titrata+ concentration and a decrease in the pH value of the sample [Streptococcus and Leuconostoc can tolerate an acidic pH range from 4.0 to 4.5, while some species of Lactobacilli and Pediococci grow around pH 3.5 [L. acidophilus, there was an inversely proportional relationship between the titratable acidity and the pH. At the end of the fermentation period, L. acidophilus retained a high viable cell count at 9.89 log CFU/mL at final pH of 3.87. Similarly, Yoon et al. [L. plantarum, L. casei, and L. delbrueckii.Acid productions (primarily lactic acid) occur after the consumption of the sugar in the fruit and vegetable juice by the LAB. As the sugars are consumed, more lactic acids are produced, causing an increase in the He sample ,19,30. Te sample , which ae sample . The LABd pH 3.5 . Yulianad pH 3.5  reportedLactobacillus bulgaricus require an optimum pH of 8.2 to function properly [L. lactis ssp. lactis to catalyze the conversion of phosphoenolpyruvate and ADP to pyruvate and ATP in glycolysis [L. casei requires a near-neutral pH of 6.5 to cleave amino acids from the N-terminus or proteins [Regardless of how remarkably tolerant the LAB are to the acidic environment, there is a certain threshold of pH values which, when exceeded will cause damage to the cells. As the fermentation media accumulate more acids, the negative feedback mechanism slows down the growth of LAB to prevent the over-acidification of the cell cultures ,168,169.properly . A neutrycolysis . The amiproteins .out) and internal pH (pHin) values, generating a proton motive force [out and pHin (pHout = pHin) values allow the maintenance of Δ pH and pH homeostasis to be performed for optimal growth. A lowering of the external pH (pH of the medium) compared to the internal pH (pH of the cytoplasm) drives the proton motive force of H+ influx into the cell [in, LAB such as Streptoccci, Lactocci, and Lactobaclli have a proton symport system called H+-ATPase to extrude protons out of the cell by ATP hydrolysis. This membrane-bound proton-translocating enzyme exports protons from the alkaline cytoplasm to the acidic fermentation medium against the concentration gradient of protons, thereby requiring energy in the form of ATP [Thus, LAB will perform pH homeostasis to cope with the increasing acidity of the fermentation medium . To mainve force . Balancethe cell . Therefom of ATP .Salmonella, L. monocytogenes, and C. botulism. Low temperatures slow down metabolism and extend the log or stationary phase of the mesophilic bacterial growth curve [Refrigeration is a way to extend the preservation of food. According to the U.S. Food and Drug Administration (FDA), the basic guidelines for storage include a working refrigerator temperature at or below 40°F (4 °C), while the freezer compartment should be 0 °F (−18 °C). Therefore, all household and laboratory refrigerator temperatures should operate at 4 °C as a means to prevent or slow down the growth of foodborne pathogens such as th curve .Bacillus psychrophilus, Chlamydomonas nivalis and Polaromonas vacuolate [Most LAB are mesophiles, while some exhibit cold-tolerant characteristics. Psychrophiles are microorganisms capable of growing at very low temperatures ranging from −12 °C to 20 °C, where the optimum temperature of 15 °C shows the best growth . Howeveracuolate . Hence, acuolate . The adaacuolate  and adeqacuolate .Lactobacillus sp. CA1 and CA4 can grow at a temperature range of −20 °C to 37 °C. The adaptation of the isolated LAB strains at low temperatures may be due to the storage of the milk inside the refrigeration temperature at the veterinary department. Milk samples are stored to preserve taste, texture, and aroma while halting the growth of pathogens. The correlation of storage temperature and adaption of bacteria to the cold environment is supported by the findings of Kato et al. [Lactobacillus algidus sp. nov. from vacuum-packaged refrigerated beef, which exhibited a psychrophilic nature due to its ability to grow on MRS agar at 0–25 °C, but no viable cells were detected at 30 °C.At low storage temperatures, researchers find it hard to maintain an ideal number of viable cells of their probiotic strains. Therefore, the selection of potential probiotic strains with the psychrophilic trait would be beneficial for the storage of probiotic beverages. Kasimin et al.  successfo et al. . They is6–107 CFU/mL [L. casei, L. plantarum, and L. delbrueckii accumulated viable cell counts of 1.1 × 109 CFU/mL, 17.5 × 108 CFU/mL, and 11.0 × 108 CFU/mL, respectively. In cold storage at 4 °C, however, it was observed that L. casei dramatically decreased in viability after one week of storage (1.1 × 106 CFU/mL) before cell death in the following weeks. On the other hand, L. delbrueckii retained the ideal viable cell count of the probiotic dose for two weeks (107–108 CFU/mL) but decreased gradually at week 3 (34.3 × 105 CFU/mL) and week 4 (4.5 × 105 CFU/mL). The study discovered that the viable cell count of L. plantarum remained at an ideal daily probiotic dose around 107–108 CFU/mL.Additionally, several studies on probioticated non-dairy beverages showed that some probiotic strains are capable of retaining an appreciable number of viable cells at low storage temperatures. To exert therapeutic effects, the probiotic drink should contain a minimum of 107 CFU/mL ,3. Yoon 7 CFU/mL  discoverLactobacillus casei shirota retained the ideal range of probiotic dose (108–109 CFU/mL) under refrigeration temperature 5 °C to 8 °C for 24 to 96 h in fresh coconut water compared to industrialized coconut water, where the probiotic viability started to decline after 72 h and reached an almost-zero viable cell count at 96 h storage. They believed that the industrial processing and addition of antioxidants altered the nutritional profile of the substrate, influencing the pH stability and acidity.Praia et al.  revealedThe development of plant-based probiotic drinks is important in promoting healthier alternatives to dairy-based drinks. A thorough grasp of the range of plant fermentable sugars, as well as the critical quality indicators of fermented products, aids in the production of high-quality functional beverages. The difficulties in maintaining the high viability of probiotics in fruit-and-vegetable-based drinks impose challenges to meet the increasing demand by consumers. The commercialization of non-dairy probiotic drinks creates a variety of taste options and promotes healthier lifestyles for consumers


The ability of an organism to voluntarily control the stimuli onset modulates perceptual and attentional functions. Since stimulus encoding is an essential component of working memory (WM), we conjectured that controlling the initiation of the perceptual process would positively modulate WM. To corroborate this proposition, we tested twenty-five healthy subjects in a modified-Sternberg WM task under three stimuli presentation conditions: an automatic presentation of the stimuli, a self-initiated presentation of the stimuli (through a button press), and a self-initiated presentation with random-delay stimuli onset. Concurrently, we recorded the subjects' electroencephalographic signals during WM encoding. We found that the self-initiated condition was associated with better WM accuracy, and earlier latencies of N1, P2 and P3 evoked potential components representing visual, attentional and mental review of the stimuli processes, respectively. Our work demonstrates that self-initiated stimuli enhance WM performance and accelerate early visual and attentional processes deployed during WM encoding. We also found that self-initiated stimuli correlate with an increased attentional state compared to the other two conditions, suggesting a role for temporal stimuli predictability. Our study remarks on the relevance of self-control of the stimuli onset in sensory, attentional and memory updating processing for WM. EEG; Enaction; Embodied cognition; ERP; Sternberg task; Temporal expectation. In contrast, in everyday situations, relevant stimuli to WM tasks may appear to our sensory system due to the agent's active control, such as what occurs when we turn the page of a book or slide the mobile screen to browse a web page. When the agent initiates the presentation of the stimuli via a motor action , behavior and its underlying neural mechanisms are modulated in several cognitive processes such as perception and attention . This idWhat are the mechanisms deployed during the self-initiation of the stimuli that can modulate WM encoding? Evidence in perceptual and attentional tasks has shown that self-initiated stimuli modulate both sensory and attentional neural processing . This fiHow does self-initiation of the stimuli modulate perception? One proposed mechanism is the modulation that motor activity exerts on early sensory cortices . It has A similar effect is observed on attentional neuronal substrates when stimulus is self-initiated. Self-initiated stimulus shows increased amplitude of both P3a and P3b components compared to passive externally paced stimulus , 2006. SAs for effects of self-initiated stimuli in memory tasks, behavioral evidence reported better novel melodies recognition in skilled pianist when the melody was learned in an auditory-motor context  than auditory-only  condition . HoweverSelf-initiated tasks usually allow for temporal prediction of the stimuli appearance since the movement that triggers the stimuli is finely coupled in time  to the sensory consequence . NeverthBased on the literature reviewed above, we hypothesized that self-initiated stimuli improve WM encoding. This effect relies on the modulation of visual, attentional, and memory updating processing. To test this hypothesis, 25 healthy adults performed a modified Sternberg WM task  with thrMoreover, to test if the self-initiated stimuli effect is based on temporal prediction, an Active Decoupled encoding condition (AD) was designed. AD consisted of the presentation of the stimuli after a random delay (400–600 ms) after the button press, which reduces the temporal predictability of the stimuli onset without affecting the sense of self-control of the stimuli  . In case2The study methods, experimental protocol and consent was approved by the Ethical Committee in Human's Research of the Medicine Faculty of the Universidad de Chile and followed the Declaration of Helsinki.2.1Twenty-six healthy adults  volunteered and were tested. All the participants were under- or postgraduate students of the Universidad de Chile with non-current or history of neurological, psychiatric, systemic disease, and normal or corrected-to-normal vision. This information was corroborated by anamnesis and the application of the MoCA test . In the 2.2Participants engaged in a modified Sternberg working memory task , with thIn all the three conditions, each trial began with an eye-tracking-based drift correction to ensure that participants' eyes positions remain similar in every trial. After drift-correction, a first fixation cross marked the beginning of each trial. The stimuli array (S1) lasted 250 ms (ms). After S1 disappeared, only the central fixation cross remained for a random time between 2000 and 2500 ms (maintaining period). Subsequently, the probe stimulus appeared for 800 ms, followed by the final fixation cross, which lasted 1500 ms . Subjects had all the retrieval time to respond (800 ms + 1500 ms = 2300 ms). If the probe was present in the S1 array, participants had to press the joystick's right back button with their right index finger. Conversely, if the probe was absent from set S1, participants had to press the left-back button with their left index finger.As stated before, to determine if self-initiated stimuli modulate WM, the onset of the S1 set was manipulated, generating three encoding conditions: AC, AD, and P. In the AC condition, we required the participants to fixate their gaze to a cross and to press a frontal button of the joystick (with either of their thumbs). Immediately after the button press, the S1 array appeared on the screen for 250 ms. The AD condition was very similar: participants fixed their gaze to a cross and then pressed a frontal button but, S1 appeared with a random delay (400–600 ms) after the button press. This time-lapse does not disturb the sense of agency of individuals . FinallyEach condition was presented in separate blocks of 100 trials. Each participant executed all the three blocks, accomplishing a total of 300 trials. The presentation order of the blocks was pseudo-randomized per participant. Instructions were given separately for each condition, at their beginning. Participants were not told about the specific difference between AC and AD conditions to not bias their answers. At the end of each block, participants 12 to 25 determined the task's difficulty. To do so, they rated their global sensation of how secure they made their answers in that particular block (hereafter "reported confidence"). We used a scale of confidence ranging from 1 to 7: 1 corresponded to "not sure of my answers," 4 was neutral confidence, and 7 corresponded to "very sure of my answers." The experimental paradigm was designed in Experiment Builder  and executed in Eyelink 1000 .2.3Viewsonic vx2753mh-led 27 inches screen . Subjects seated at a 72 cm distance from the screen. At this distance, 40.35 pixels equals 1 visual degree (°). Images were 45.1° wide and 26.3° high. Subjects kept their chin on a chinrest during the whole experiment to reduce head movements and to maintain a stable distance between the eyes and the screen. We monitored fatigue blinks and eye positions with an Eyelink 1000  eye-tracking system.The stimuli were presented in a flat The background was set to gray (127/127/127 RGB) during the whole session to avoid luminance changes. All the stimuli (S1 and probe stimulus) were black consonants. S1 consisted of five capital consonants (except “X” and “Y”) presented simultaneously in a circular array surrounding a fixation cross . The fix2.4To assess if self-initiated stimuli do influence WM, we analyzed the encoding condition's effect on accuracy and reaction times (RT). We defined accuracy as the proportion of correct answers (True Positives and True Negatives) for each participant. RT corresponded to the time (in ms) elapsed since the appearance of the probe until the participant's answer. RT was calculated using only correct trials. We tested the effects of the encoding condition on the two dependent variables using two One-way-repeated ANOVA. We computed post hoc tests for each variable with significant effects using a paired-t-test corrected by the Holm-Sidak method. We chose these parametric tests according to results of normality distribution (assessed by Shapiro-Wilk test) and homoscedasticity (assessed by Levene test) of both accuracy and RT (See supplementary Table S1). We also analyzed the reported confidence rating using the Friedman rank sum test. This result can be generalized to the whole sample, since the sub-sample with reported confidence ratings has no statistically significant difference with the sub-sample without reported confidence ratings in terms of accuracy, RT, MoCA results, and age (see supplementary Table S2).Next, we evaluated the impact of covariates on WM behavior. We identified four variables that could potentially be influencing performance. These covariates are: task learning, block position, uncontrolled pre-stimuli time and total pre-stimuli time. We defined task learning as the change in the probability of making a correct response due to the increasing trial number (from 1 to 100). The block position was a categorical variable, and its value could be the first, second, or third block. We defined the uncontrolled pre-stimuli time as the time before the stimuli onset whose duration was not controlled by the subjects. In the passive condition, the uncontrolled pre-stimuli time was the duration of the first fixation cross. In the AD condition, the uncontrolled pre-stimuli time was the duration of the time delay between the button press and the onset of the S1 set. In the AC condition, the uncontrolled pre-stimuli time was zero ms. The total pre-stimuli time corresponded to the uncontrolled pre-stimuli time plus the time taken by the participants to press the button in the AC and AD conditions; in P condition this value was equal to the uncontrolled pre-stimuli time.Since the stimulus onset time is different for each condition, we first explored the influence of the uncontrolled pre-stimulus time on accuracy. To do so, we first computed the mean accuracy and the 95% confidence interval along the time-series for the AD and P conditions separately D, and peNext, we wanted to measure the main effect of each of the covariates (as well as their interactions) over accuracy. To do so, we computed six separate binomial General Linear Mixed Model (GLMM) . We set 2.5We recorded Electroencephalographic (EEG) activity at a 2048 Hz sample rate using a BioSemi Inc. amplifier of 32 active scalp electrodes. We used the Common Mode Sense (CMS) and the Driven Right Leg (DRL) electrodes as ground electrodes. Subjects used head caps to hold the electrodes according to the 10/10 system . Eight eWe offline re-referenced the continuous EEG signal to the average activity of the 32 electrodes. Then, we filtered the signal using a FIR symmetric passband filter between 0.5 and 40 Hz with a linear phase. Its design is firwin with a Hamming window . The size of the filter was 6.6 s. The transition bandwidth of the filter was 0.5 Hz in the lower frequency limit and 10 Hz in the upper-frequency limit. The passband ripple of the filter was 0.0194 dB and the stopband attenuation was set at 53 dB. We discarded noisy channels by visual inspection and then interpolated using spherical splines. None of the channels of interest  were interpolated. After that, we performed an Independent Component Analysis (ICA) to determine and eliminate components related to blinks and eye movements. We automatically eliminated segments containing muscle artifacts and other artifacts unrelated to blinks through a 500 μV peak-to-peak rejection threshold. This preprocessing stage was done using MNE-Python .2.6P100-like, N100-like, P200-like, and P300-like . We used electrode Oz to calculate P1 and N1 components  present in the AC condition. We then explored ERP components related to those processes to analyze the influence of self-initiation of the stimuli in early visual, attentional, and memory updating processing. Components of interest were mponents , Fz to cmponents , and Pz mponents . All ERPmponents . The avemponents .2.7To test if there was an effect of the encoding condition over amplitude and latency, we performed either one-way ANOVA or Friedman tests (one per ERP component), based on the results of normality and homoscedasticity. The factor corresponded to CONDITIONS in both cases. The rank-sum Wilcoxon or t-test corrected by Holm-Sidak was performed as a Post Hoc test.Results). Next, we seeked to test if the relationship between the latency of P2 and accuracy was dependent on the encoding condition. To do so, we performed a Linear Mixed Model (LMM). The model included accuracy as a dependent variable, the interaction between P2 latency and the encoding conditions as a fixed effect, and subjects as random effect variable.To evaluate which neuronal marker is related to the accuracy improvement found in the voluntary self-initiated condition, we used a conditional Classification Tree (CART model). CART models allow us to classify or estimate phenomena with discrete changes . For thiThen, we used a second CART model to assess which electrophysiological variable distinguishes better among encoding conditions. The partitioning variables used to characterize the encoding conditions were accuracy, RT, and the amplitudes and latencies of P1, N1, P2, and P3 components.We performed all statistical computations using RStudio . Rstudio libraries used were EZ (v4.4-0)  to run A3Materials and Methods).We hypothesized that self-initiated stimuli presentation improves WM performance through the temporal modulation of visual, attentional, and memory updating processing. To evaluate how self-initiated stimuli would affect WM encoding, twenty-six participants performed a Sternberg working memory task with three different stimuli's encoding conditions. In the Active Coupled (AC) condition, the stimuli were presented immediately after the participants pressed a button. In the Active Decoupled condition (AD), stimuli were presented with a delay of 400–600 ms after the button press, which allowed us to test whether possible effects of self-initiated stimuli are tied to temporal precision of stimuli onset. Finally, these two self-initiated conditions were contrasted with the passive presentation of the stimuli (P), where stimuli were automatically presented between 600-1000 ms after the first fixation onset. Each encoding condition consisted of one block of 100 trials so that each subject executed three separate blocks (one per encoding condition) (See We tested the proposed hypothesis by two approaches: i) Is behavior modulated by these three experimental conditions? and ii) Does self-initiation modulate early visual, attentional, and memory updating processing during encoding?3.1participants).  = 25.67, p = 2.6e-08; η2G = 0.2). No encoding condition effects were found on RT  = 1.45, p = 0.244)  on performance (RT and accuracy). We assessed this in twenty-five of the twenty-six recruited subjects  . These r= 0.244) A.Table 1(24) = 8.21; p = 5.9e − 08, Cohen's d = 1.64). To further investigate if the timing between motor acts and its sensory consequences is relevant to the self-initiation effect, we compared the accuracy between AC and AD conditions . Paired t-tests corrected by Holm-Sidak method also confirm better accuracy in AC compared to AD condition (t(24) = 3.25; p = 0.003, Cohen's d = 0.65). Noteworthy, when subjects engage in a passive WM task, their accuracy is poorer when compared to both active conditions (AD compared to P: t(24) = 3.56, p = 0.003, Cohen's d = 0.71). This indicates the impact of self-initiated stimuli on WM performance, which seems to be partially linked to action and task-relevant stimuli coupling. A remarkable point is that these accuracy effects are not explained by differences in the perceived difficulty of the task since there are no statistically significant differences in their reported confidence, rated by the participant at the end of each condition block  = 1.47; p = 0.478)  = [0.81–0.86]) and P conditions . Paired t-tests corrected by the Holm-Sidak method show that AC has better accuracy than P condition (t= 0.478) C.β = 1.42e − 0.1 ± 5.35e − 02, t = 2.67, p = 0.008; P: β = 4.422e − 0.2 ± 2.96e − 02, t = 1.49, p = 0.136). These results show that the uncontrolled pre-stimuli time influences AD accuracy and, therefore, could be a good predictor of accuracy.We then explored the impact of other cognitive factors on performance. Since the stimuli onset time is variable between conditions, we first assessed the influence of this time on accuracy. β = 0.20 ± 0.09, z = 2.25, p = 0.024), block position , and the encoding conditions  . This implies that, when other predictors are also considered, the probability of making a correct response does not significantly depend on the time that the stimuli take to appear in the AD and P conditions. We find a non-significant trend for the interaction between uncontrolled pre-stimuli time and AD condition . We did not find a significant interaction between task learning and block position , nor interaction between task learning and tasks . These GLMM results corroborate the previous ANOVA analysis, indicating a significant decrease in the probability of making a correct response when subjects are engaged in the AD or the P conditions compared to AC condition. The probability of making a correct response falls 0.779 log-odds points in the AD condition, and the P condition's 1.142 log-odds point. Alongside the task learning effect, results show that the probability of making a correct response rises in 0.2 log-odds points with each trial (from 1 to 100) within one block. Results also yield no interaction between task learning and encoding conditions, showing that task learning has statistically no different effect on all three conditions. Likewise, the model reveals that the probability of answering correctly increases in 0.139 log odds with the block position. Like in task learning, the effect of the block position is also independent of the encoding condition. Finally, our results show that the differences in the onset stimuli time that is not under the control of the subject impacts non-significantly on accuracy, with a trend to increase the probability of making a correct answer in 0.263 log-odds. Altogether, GLMM yields other factors such as task learning and block position that influence performance alongside the encoding conditions effects , including encoding condition, uncontrolled pre-stimuli time as well as two other covariates . These covariates included task learning  and block position. This model yielded the best AIC compared to other models tested (supplementary Table S2). The model results show a significant effect in three of the independent variables assessed: task learning (4e − 04) D. On the effects E.In summary, behavioral analyses show that coupled self-initiation of the stimuli in a WM task increases the probability of performing correctly, even though other timings and learning factors also modulate this probability. Remarkably, self-initiation improves performance, even when the time between cue onset is less favorable. In other words, even when longer uncontrolled pre-stimuli times correlates with higher accuracy scores, active conditions (whose uncontrolled pre-stimuli times are shorter than passive) perform better than passive.3.2We then assessed whether self-initiated stimuli presentation impacts visual, attentional, and memory updating processing during WM encoding. To do so, the amplitudes and latencies of related ERP indexes were analyzed . P1 component is a known marker of lateral extraestriate visual cortex ; N1 comp = 52.43, p = 2.16e − 11; η2G = 0.468) and the P2 components  = 36.39, p = 2.262e − 09, η2G = 0.459). Friedman rank sum test yield a significant effect of the encoding conditions on P1 latency  = 23.38, p = 8.34e-06, ωkendall = 0.6869) and P3 latency  = 10.91, p = 0.004, ωkendall = 0.893). No statistically significant effect of encoding conditions was found on peak-to-peak amplitude of all the components analyzed  = 5.05, p = 0.079; P2: χ2 = 0.32, p = 0.853; P3: χ2 = 0.74, p = 0.691). These results suggest a modulatory effect of the encoding conditions on the temporal domain of sensory, attentional processes and memory updating mechanisms. In The ANOVA results reveal a significant effect of the encoding conditions on the latency of the N1 (F(18) = −10.51; p = 1.2e − 08, Cohen's d = −2.41), P2 (t(18) = −7.71; p = 9.3e − 13; Cohen's d = −1.77) and P3 . On the other hand, P condition has the latest latency in P1  and P2 (AD/P: t(18) = −2.87; p = 0.001, Cohen's d = −0.66) components. Nevertheless, paired tests corrected by Holm-Sidak show no statistical differences between AD and P conditions in the N1 component (t(18) = −1.33; p = 0.2), and in P3 component . These results suggest an effect of voluntary self-initiation of the stimuli on sensory, attentional memory updating processing, which is stronger when the voluntary action is temporally coupled to the task-relevant sensory consequence.If self-initiated stimuli accelerate sensory, attentional and memory updating processing of WM, self-initiated conditions (AC and AD) should show earlier latencies compared to passive condition. Our results yield that the subjects have earlier latencies in AC compared to P condition in P1 , N1 (paired t-test corrected by holm: t(18) = −7.35; p = 8.01e − 07, Cohen's d = −1.69), P2 (t-test: t(18) = −5.22; p = 7.9e − 09, Cohen's d = −1.2) and P3 . As in behavioral analysis, these ERP results suggest that the self-initiated stimuli effect is partially linked to the temporal coupling between action and the task-relevant sensory consequence.To further examine if the temporal coupling between motor acts and its sensory consequences is important to the self-initiation effect, we analyzed the latency differences between AC and AD conditions. Paired tests corrected by Holm-Sidak shows earlier latencies of AC compared to AD in P1 , N1 , the attentional processes (P2 component) and the memory updating mechanism (P3 component) engaged during encoding in WM. Moreover, no significant effects on peak-to-peak amplitudes were observed. Results also show that delays between movements and their sensory consequences (as reflected in AD compared to AC condition) do not yield the same effect on encoding processing. Nevertheless, on comparing AD to passive condition, the latencies of P1 and P2 components appear earlier, suggesting that voluntary motor acts modulate WM, even if they are not coupled to the task-relevant stimuli, nor can they finely predict the stimuli onset time.3.3We then aimed to investigate if earlier visual and/or attentional processing are related to the accuracy improvement found in the voluntary self-initiated condition. To assess this, we performed a conditional Classification Tree (CART model) . The parAccording to the CART model result, accuracy can be divided into two groups, higher performance (median = 0.82) and lower performance (median = 0.733), according to the latency of the P2 component A. When tGiven that the above-mentioned result occurs independently from the encoding conditions, we then evaluated if this outcome persists when it is examined by the encoding conditions. P2 latencies from the AC condition (median = 170.79 ± 11.19 ms) show higher accuracy values B. On theIn summary, our results show that P2 latency is inversely related to WM accuracy, with shorter latencies corresponding to better outcomes. Moreover, this relation is dependent on the encoding condition, which is consistent with our ANOVA results showing a task effect on accuracy. This suggests that acceleration of attentional processing is a mechanism related to the WM improvement in voluntary self-initiated conditions.3.4Next, we explored whether the earlier deploying time of visual and/or attentional processing provides reliable markers of voluntary self-initiation of the stimuli. To do so, we performed a CART model to test how reliably we can estimate the encoding condition of a trial from the latency of its associated ERP components. Since CART models are ideal for classifying or evaluating discrete state variables , by usinCART model result shows that the latency of the N1 component better distinguishes between encoding conditions, using a cutoff of 174.316 ms (p < 0.001) See A. No othSince earlier N1 latency is the variable that better distinguishes AC, and that AC is also the condition that shows better accuracy See B, we ana4The current study assesses whether self-initiated stimuli presentation modulates WM encoding through temporal modulation of visual and attentional processing. This was investigated by evaluating the influence of active self-initiated stimuli on behavior in a Sternberg working memory task while concurrently measuring ERPs’ encoding components widely used as indexes of visual  to avoid artificial differences between the amplitudes.Altogether, our data suggests that self-initiated stimuli effect enhances WM performance and engages a faster attentional state related to WM encoding.4.3Our results open further questions regarding the mechanisms involved in the self-initiated stimuli effect, suggesting the involvement of motor control systems and temporal prediction mechanisms. Nevertheless, our current task design cannot disentangle the contribution of each process. Our study suggests that fine temporal coupling between the motor act and the relevant stimuli triggered has a greater impact on WM processing than timely decoupled motor-sensory consequences. Nevertheless, it remains unclear what is the minimal amount of time-decoupling needed to the effect to remain maximum. In the somatosensory domain, just 100 ms motor-sensory decoupling are sufficient for modifying perception . Future Moreover, attention fluctuates in time   and postRemarkably, the motor system can modulate cognition not only using direct corticocortical connections but also through modulatory effects of motor activation on dopamine, acetylcholine, and noradrenergic circuits. The dopamine system's importance in WM functioning has been extensively documented . For insCritically, the networks mentioned above require several synaptic relays to take place and thus could unravel more slowly than the modulatory effects of direct motor-sensory/associative cortices projections. Accordingly, the catecholamine system's recruitment and basal ganglia circuits by motor systems during active self-initiated encoding could explain the discrepancy between decoupled self-initiated encoding and passive encoding in both behavior and electrophysiology. On the other hand, fast direct cortico-cortical projections can explain the distinction between time-coupled and decoupled self-initiated encoding. While stimuli appearing coupled to the movement would be modulated by both fast (direct motor projections) and slow  modulatory networks, stimuli presented hundreds of milliseconds after the movement has been executed would be influenced by the slow response modulatory networks only. This would explain that both coupled and decoupled self-presentation lead to improved WM performance but that the effect is stronger in the coupled condition.5To conclude, the current findings show that self-initiated stimuli effect modulates WM encoding processing through the acceleration of early sensory and attentional processes. Performance enhancement in coupled self-initiation of the stimuli onset is related to an earlier attentional state, which seems to be absent in passive states and active decoupled self-initiated states. Nevertheless, processing facilitation of early sensory and attentional processes is also present in decoupled self-initiation compared to the stimuli's passive triggering. This suggests that active self-initiation, regardless of whether it is time-locked or not, engages a motor-mediated modulation on cognition in addition to the temporal predictive mechanism (the latter being absent in the decoupled self-initiated condition). Finally, our study also remarks that sensory and attentional processing during encoding are crucial components of WM.Rocio Loyola-Navarro, Pedro E. Maldonado: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Cristóbal Moënne-Loccoz, Rodrigo C. Vergara: Analyzed and interpreted the data; Wrote the paper.Alexandre Hyafil: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Francisco Aboitiz: Conceived and designed the experiments; Wrote the paper.Rocio Loyola-Navarro was supported by Beca Doctorado Nacional [21150153], Agencia Nacional de Investigación y Desarrollo. Support from ANID/ PIA/ Basal Funds for Centers of Excellence [FB0003] is gratefully acknowledged.Pedro E. Maldonado was supported by Iniciativa Científica Milenio, ICM [ICN09_015], Agencia Nacional de Investigación y Desarrollo [FB210017], and BNI - ACE [210007].Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


P < 0.0001). In the light of large numbers of people worldwide remaining unvaccinated and continuing uncertainty regarding vaccine efficacy over time and against variants of concern, identification of people at high risk of severe COVID-19 may encourage the uptake of vaccinations (including boosters) and the use of non-pharmaceutical inventions.Using nested case–control data from the Lifelines COVID-19 cohort, we undertook a validation study of a clinical and genetic model to predict the risk of severe COVID-19 in people with confirmed COVID-19 and in people with confirmed or self-reported COVID-19. The model performed well in terms of discrimination of cases and controls for all ages (area under the receiver operating characteristic curve (AUC) = 0.680 for confirmed COVID-19 and AUC = 0.689 for confirmed and self-reported COVID-19) and in the age group in which the model was developed . There was no evidence of over- or under-dispersion of risk scores but there was evidence of overall over-estimation of risk in all analyses (all  Severe coronavirus disease 2019 (COVID-19) disproportionately affects older adults, but can occur in people of all ages, especially those with comorbidities [We previously described a clinical and genetic model for predicting severe COVID-19 that was developed and validated using data from the UK Biobank . This moWe used data from participants in the Lifelines COVID-19 cohort  who wereDuring 2020, questionnaire links were emailed to Lifelines and Lifelines NEXT participants, weekly from the end of March to mid-May and then every 2 weeks until July, after which the questionnaires were sent monthly through to April 2021 . LifelinCOVID-19 vaccinations became available in the Netherlands in mid-January 2021 and were initially offered to high-risk groups and then progressively to other groups (such as care workers) and younger age groups until all adults became eligible in mid-June 2021 . From quGiven the limited availability of testing early in the data collection period, the confirmed COVID-19 group is likely to miss some participants who had COVID-19. Conversely, the broader group including participants with self-reported COVID-19 is likely to have some false positives. The true number of participants who had COVID-19 will be somewhere between the two. Therefore, we conducted two sets of analyses: (i) using participants with confirmed COVID-19 and (ii) using participants with confirmed and self-reported COVID-19.As we did previously, we used hospitalisation as a proxy for severe COVID-19 . The Lifβ coefficient for each of the risk factors listed in Supplementary Table S1. The probability of severe COVID-19 is then the inverse logit of the log odds (x), that is, 1/(1 + ex−).The risk factors included in the calculation of the risk of severe COVID-19 are age; sex; body mass index; a history of cerebrovascular disease, diabetes, haematological cancer, non-haematological cancer, hypertension, kidney disease or respiratory disease (excluding asthma); and the genotypes of seven SNPs – rs112641600, rs10755709, rs118072448, rs7027911, rs71481792, rs112317747 and rs2034831 [We used the age reported at the completion of the participant's first Lifelines COVID-19 questionnaire. The questionnaires asked about a history of cancer, cerebrovascular disease, diabetes, hypertension, kidney disease and respiratory disease on three occasions. If any of the participants' responses to the risk factor questions were missing for all answered questionnaires, we used responses from their Lifelines baseline questionnaire.r2 = 0.75, D′ = 1.0) and rs78654835 was the proxy for rs112317747 ). To extend the model to people aged less than 50 years, we estimated the risk associated with younger age groups using data from the Centers for Disease Control and Prevention [We were not able to identify the type of cancer reported by Lifelines COVID-19 participants so we used the risk associated with having a non-haematological cancer for all reported cancers. In the Lifelines questionnaires, the respiratory disease question included asthma, whereas this is excluded in the model calculations. Because we were not able to distinguish respiratory disease solely due to asthma, we included all reports of respiratory disease in the model calculations. Gender, ethnicity, weight and height were taken from the Lifelines baseline questionnaire. If two weight or height measurements were available, we used the most recent weight measurement and the mean of the height measurements. Two of the SNPs in the risk model were not available on the Illumina CytoSNP-12v2 array used by Lifelines . InsteadIn each analysis – (i) using participants with confirmed COVID-19 and (ii) using participants with both confirmed and self-reported COVID-19 – the cases were those who reported having been hospitalised for COVID-19 and the controls were the remainder of the group. We also did analyses restricting the dataset to those aged 50 years or older (the ages in which the model was developed). As we did previously , we assehttps://www.lifelines.nl/researcher/how-to-apply). Stata MP Version 13.1 code for the analysis is available for non-commercial purposes from the corresponding author on request.The Lifelines protocol has been approved by the Medical Ethical Committee of the University Medical Center Groningen, The Netherlands, under Approval Number 2007/152. All participants provided written informed consent to Lifelines before data collection began. This research was conducted using Lifelines data under Project Number OV20-00101. The data used in this study were made available to us by Lifelines and are not publicly available. Researchers can apply to use the Lifelines data used in this study, and more information about how to request Lifelines data and the conditions of use can be found on their website  completed one questionnaire, 5742 (21.4%) completed 2–5 questionnaires, 4194 (15.6%) completed 6–10 questionnaires, 3568 (13.3%) completed 11–15 questionnaires, 6106 (22.7%) completed 16–20 questionnaires and 4021 (15.0%) completed 21–23 questionnaires. We excluded 15 933 questionnaires from 15 040 participants where (and after which) the participant reported being vaccinated.s.d.) = 10.3) and the mean number of completed questionnaires was 17.0 (s.d. = 6.3). In the confirmed COVID-19 control group, there were 905 (66.8%) women and 450 (33.2%) men; their mean age was 53.0 years (s.d. = 11.6) and the mean number of completed questionnaires was 14.1 (s.d. = 6.8). In the confirmed and self-reported COVID-19 control group, there were 2414 (62.3%) women and 1459 (37.7%) men; their mean age was 51.5 years (s.d. = 11.8) and the mean number of completed questionnaires was 12.2 (s.d. = 7.2). In the cases, the mean probability of severe COVID-19 was 0.225 (s.d. = 0.019); in the confirmed COVID-19 controls, the mean was 0.165 (s.d. = 0.002); and in the confirmed and self-reported COVID-19 controls, the mean was 0.165 (s.d. = 0.001). The risk distribution for the cases, both control groups and the whole Lifelines COVID-19 cohort, is show in Supplementary Figure S1.In the final dataset, 55 participants were hospitalised for their COVID-19 infection and were considered cases in this study. We used two control groups: the first comprised the 1355 participants who had confirmed COVID-19; the second comprised both the first control group and the 2518 participants who self-reported having had COVID-19 (i.e. 3925 participants). In the cases, there were 28 (50.9%) women and 27 (49.1%) men; their mean age was 57.6 years , as in the original paper (β = 0.90). In both groups, the model overestimated risk , whereas the validation group in the model development paper did not (α = −0.08).The top half of The bottom half of The true number of people with COVID-19 is unknown but is likely to be somewhere between the number who test positive for SARS-CoV-2 infection and the number who self-report having had COVID-19. In this study we have addressed this uncertainty by conducting two sets of analyses: the first in individuals with confirmed COVID-19, and the second in individuals with confirmed and self-reported COVID-19. In terms of discrimination, the AUC of the risk prediction model was almost identical in the two analyses and only slightly lower than the AUC in validation group in the model development paper . This anIt is possible that some severe cases of COVID-19 have not been ascertained in this dataset. Death registry linkage identified 77 deaths in the broader Lifelines COVID-19 cohort in people who did not have confirmed or self-reported COVID-19. While these deaths may have been unrelated to COVID-19, some will represent people who became infected and were too unwell to complete a Lifelines COVID-19 questionnaire before they died. This limitation may have attenuated some of the results seen in this study.As the pandemic continues to evolve, there are two major issues that can affect the utility of our risk model. First, we have to address the impact of viral variants on the performance of the risk model. The model development paper  and the Second, our model does not incorporate the protection offered by vaccination. Thus in vaccinated adults, the model will overestimate their risk of developing severe disease. While the model was well calibrated in the validation dataset from the UK Biobank . The oveHerein, we have validated our model to predict risk of severe COVID-19 if infected with SARS-CoV-2 in a dataset unrelated to the one in which the model was originally developed and validated. Despite new SARS-CoV-2 variants of concern, the model may complement current public health efforts in vaccine (and booster) uptake. The model may also enable healthcare providers to have more informed discussions with patients about their risk-mitigation options and early treatment awareness, if ever infected.


Non-suicidal self-injury (NSSI) is the intentional and repeated direct injury to one’s bodily tissues or organs without the intent to die, which is not socially sanctioned and does not result in death. This study will be the first to explore the relationship between NSSI behavior and thyroid hormone and sex hormone levels in male adolescents with depression.Among the inpatients in the children’s ward of Shandong Mental Health Center, eighty male patients with first-episode depressive disorder were randomly selected. Forty male adolescent depressed patients with NSSI behaviors were set as the NSSI group, and forty male adolescent depressed patients without NSSI behaviors were set as the No-NSSI group. Their thyroid hormones  and sex hormones  were measured, and the severity of self-injury in the NSSI group was assessed using the Adolescent Self-Injury Questionnaire. The NSSI group was tested again after 6 weeks of sertraline treatment for biological indicators and assessed by the Self-Injury Questionnaire to compare the hormonal differences between the NSSI group and the No-NSSI group and compare the differences of each index before and after treatment in the NSSI group.p = 0.001) and FT3 (p = 0.023), TSH levels (p < 0.001) were lower in the NSSI group than in the No-NSSI group before treatment, and FT4 (p = 0.036) and T (p < 0.001) levels were higher than in the No-NSSI group. T3/T4 levels were higher in the NSSI group after treatment (p < 0.001). FT4 (p < 0.001) and T (p = 0.001) levels and self-injury questionnaire scores (p < 0.001) decreased after treatment in the NSSI group. In the NSSI group at baseline, FT4 levels were negatively correlated with self-injury questionnaire scores  and testosterone levels were positively correlated with self-injury questionnaire scores , and in the NSSI group after treatment, FT4 difference was negatively correlated with self-injury questionnaire score reduction rate , and testosterone difference was positively correlated with self-injury questionnaire score reduction rate . Logistic regression analysis showed that low TSH and high testosterone levels were independent risk factors for the development of non-suicidal self-harming behaviors in male adolescent depressed patients.T3/T4 (Changes in thyroid hormone and sex hormone levels may be associated with non-suicidal self-injurious behavior in male adolescent depressed patients. Non-suicidal self-injury (NSSI) is the intentional and repeated direct injury to one’s bodily tissues or organs without the intent to die, which is not socially sanctioned and does not result in death . In receSelf-injury is a complex behavior that is the result of the intersection of psychosocial , behavioConsidering that the relationship between NSSI behavior and thyroid hormones and sex hormones is poorly understood, we conducted this study to investigate the possibility of a relationship between NSSI behavior and the hypothalamic-pituitary-thyroid (HPT) axis and the hypothalamic-pituitary-gonadal (HPG) axis.This study randomly selected 80 male patients with first-episode depressive disorder who were hospitalized in the children’s ward of Shandong Mental Health Center from October 1, 2021 to January 31, 2022. The inclusion criteria were (1) aged between 12–18 years and never having received any medication or psychosocial interventions prior to this time, and (2) meeting the diagnostic criteria for persistent depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The exclusion criteria were as follows: (1) current or previous history of other psychiatric disorders; (2) comorbid organic brain disorders, traumatic brain injury, etc.; (3) disorders affecting thyroid function and testicular function ; (4) previous history of suicide attempts (self-injurious behavior with the intent to end life); and (5) current or previous history of substance dependence or abuse. According to the diagnostic criteria of non-suicidal self-injury in DSM-5, 40 male adolescent depressed patients with NSSI behaviors were set as the NSSI group, and 40 male adolescent depressed patients without NSSI behaviors were set as the No-NSSI group.This study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of Shandong Mental Health Center (2021-R102). All patients and family members participating in the study were informed and voluntarily signed the informed consent form.Ottawa self-injury inventory (OSI): The OSI is a self-rating scale containing 28 items to rate the frequency, method, and motivation of self-injury in the past year and addresses self-injurious behavior in response to negative emotions, stressful events, and self-injury addiction . This scThe Modified version of Adolescents Self-Harm Scale: The development of this questionnaire was based on the Functional Assessment of Self- Mutilation (FASM) by Lloyd et al.  and lateHamilton Depression Scale-17 item version  HAMD-17.A questionnaire was administered to the patients on the first day after admission. Fasting venous blood was collected in the early morning of the next day, and patients were asked to fast and abstain from food and water after 12:00 a.m. on the day before blood collection. All peripheral blood samples were collected and centrifuged for 10,000 rmp/min for 10 min at 4°C, and then all blood samples were tested for sex hormones and thyroid hormones using the i2000SR fully automated chemiluminescence immunoanalyzer developed by Abbott, USA. All samples were analyzed by the same analyst, who was blind to the sample sources. The adolescent self-harm questionnaire was retested after 6 weeks, and the subtraction rate was calculated as (pre-treatment score–post-treatment score)/pre-treatment score. All biological indicators were reviewed.Thyroid function was assessed according to free triiodothyronine (FT3), free thyroxine (FT4), thyrotropin (TSH), and FT3/FT4 ratio. Gonadal function was assessed according to estradiol (E2), progesterone (Prog), and testosterone (T). The reference ranges were 1.80–4.20 pg/ml for FT3, 0.87–1.85 ng/dl for FT4, and 0.50–7.30 μIU/ml for TSH. The reference ranges were 0–84 pg/ml for E2, 0.10–0.84 ng/ml for Prog, and 6.27–27.98 nmol/L for T.The current use of antidepressants in children and adolescents is dominated by selective serotonin reuptake inhibitors (SSRIs), and sertraline is the most commonly used . Clinicat-test, while continuous data with a non-normal distribution are expressed as the median  with a non-parametric test. Differences between the two groups were tested by independent sample t-test, and within-group changes before and after treatment were analyzed by paired sample t-test; the correlation between hormone levels and the severity of self-injury was tested by Spearman correlation analysis. The ratios that differed between groups were included in a binary logistic regression equation to analyze and explore the relationship between self-injury and hormones. Receiver operating characteristic (ROC) curves were used to analyze the value of each hormone in assessing self-injurious behavior.SPSS 26.0 was used for data analysis, and continuous data with a normal distribution are expressed as the mean ± standard deviation (±s) with a P > 0.05).The mean age of the NSSI group was 15.00 ± 2 and the mean years of education was 7.78 ± 2.213, while the mean age of the No-NSSI group was 15.50 ± 2 and the mean years of education was 7.98 ± 1.132, with no significant differences when compared (p = 0.023), the level of FT4 was higher in the NSSI group than in the No-NSSI group (p = 0.036), T3/T4 in the NSSI group was lower than that in the No-NSSI group (p = 0.001), and the level of TSH was lower in the NSSI group than in the No-NSSI group (p < 0.001). The testosterone level in the group was higher than that in the No-NSSI group (p < 0.001), and the HAMD-17 score in the NSSI group was higher than that in the No-NSSI group (p = 0.003).As shown in p < 0.001), T3/T4 was higher (p < 0.001), the level of testosterone was decreased (p = 0.001), and the score of the self-injury questionnaire was decreased (p < 0.001).As shown in r = −0.459, p = 0.003), testosterone levels were positively correlated with self-injury questionnaire scores , and FT3, TSH, E2, and Prog levels were not significantly correlated with self-injury questionnaire scores (P > 0.05). After treatment in the NSSI group, FT4 difference was negatively correlated with self-injury questionnaire score reduction rate , while testosterone difference was positively correlated with self-injury questionnaire score reduction rate , and FT3, TSH, E2, and Prog differentials were not significantly correlated with self-injury questionnaire score reduction rate (P > 0.05). See In the NSSI group, FT4 levels were negatively correlated with self-injury questionnaire scores at baseline  axis is a neuroendocrine system that regulates the release of gonadotropin-releasing hormone, gonadotropin, and sex hormones through feedback and negative feedback between the hypothalamus, pituitary gland, and gonads. At this stage, an increasing number of researchers are focusing on the relationship between sex hormones and depression. Depression shows gender differences both in terms of prevalence and symptoms , and thiThis study found that testosterone levels were significantly higher in male adolescent depressed patients with NSSI behaviors than in male adolescent depressed patients without NSSI behaviors, and the severity of self-injurious behaviors was positively correlated with testosterone levels. A recent analysis of data showed that long-term testosterone use leads to emotional instability and that testosterone use is independently associated with intentional self-harming behavior , a findiAt this stage, there are more studies on testosterone levels and suicidal behavior, and in 2012, Sher et al.  suggesteWe showed that TSH and testosterone levels can predict the occurrence of NSSI behaviors by analyzing ROC curves. In this study, we found that the best cutoff values for TSH and testosterone to predict NSSI behavior were 3.225 μIU/ml and 23.995 nmol/L, respectively. No studies have reported cutoff values for hormone levels to infer self-injurious behavior, and our findings could provide more sensitive biological markers for clinical purposes.There are some shortcomings in this study. First, our sample size was relatively small, and the observation time was short; second, we did not collect healthy people as controls, which may have some influence on the study results; again, the scales we used were mostly self-assessment scales, and recall bias may occur when answering, which may also have some influence on the accuracy of the results.This study suggests that changes in thyroid hormone and sex hormone levels may be associated with non-suicidal self-injurious behavior in male adolescents with depression. This study found that male adolescent depressed patients with NSSI behaviors had higher FT4 and testosterone levels and low FT3 and TSH levels compared to male adolescent depressed patients without NSSI behaviors. The severity of self-injury was negatively correlated with FT4 levels and positively correlated with testosterone levels. And, low TSH and high testosterone levels were independent risk factors for the development of non-suicidal self-injurious behavior in male adolescent depressed patients. In summary, changes in thyroid and gonadal hormone levels should be closely monitored when psychiatrists treat NSSI behaviors.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by Shandong Mental Health Center. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.JM participated in the design of the study, analyzed the data, and wrote this manuscript. MZ, GN, ZW, and SJ did the online survey, data collection, and logic checking. ZL revised the manuscript. All authors reviewed and approved the manuscript.